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Record W2532445215 · doi:10.3354/esr00779

Using tri-axial accelerometers to identify wild polar bear behaviors

2016· article· en· W2532445215 on OpenAlexafffund
Anthony M. Pagano, KD Rode, Amy Cutting, MA Owen, Shannon Jensen, Jasmine V. Ware, CT Robbins, GM Durner, TC Atwood, Martyn E. Obbard, KR Middel, Gregory W. Thiemann, TM Williams

Bibliographic record

VenueEndangered Species Research · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsYork UniversityTrent UniversityMinistry of Natural Resources and Forestry
FundersNatural Sciences and Engineering Research Council of CanadaMinistry of Natural ResourcesU.S. Geological SurveyYork UniversityInternational Association for Bear Research and ManagementU.S. Fish and Wildlife ServiceSan Diego Zoo Institute for Conservation ResearchWashington State UniversityUniversity of California, Santa CruzOntario Ministry of Natural Resources and ForestryWorld Wildlife FundNational Science Foundation
KeywordsAccelerometerEndangered speciesChristian ministryGeographyLibrary scienceEcologyArchaeologyBiologyComputer scienceHabitatPolitical science

Abstract

fetched live from OpenAlex

Knowledge of an animal's behavior can inform species conservation and management by revealing how individuals respond to environmental conditions (

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.200
GPT teacher head0.411
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations86
Published2016
Admission routes2
Has abstractyes

Explore more

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